{
  "id": 19078707,
  "title": "Logit Laundering: Evading Data-Use Auditing in Large Language Models",
  "url": "https://urgent.news/2026/11/06/logit-laundering-evading-data-use-auditing-in-large-language-models",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-06T13:20:30.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.09446v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Data-use auditing in Large Language Models (LLMs) aims to determine whether the given data has been used for training, thereby supporting data ownership safeguards. A dominant solution is data watermarking, which embeds traceable signals into training data and detects them in model outputs. Recent studies have proposed many watermarking-based auditing methods in LLMs. However, systematic…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}